Professional-Data-Engineer Dumps Free Test Engine Player Verified Updated [Jul 10, 2023]
Q&As with Explanations Verified & Correct Answers
Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam is a highly-revered certification exam that is designed to test individuals' ability to design, build, and manage data processing systems. Professionals who pass Professional-Data-Engineer exam are recognized as experts in the field of data engineering and are highly sought after by leading tech companies worldwide. Professional-Data-Engineer exam is intended for individuals who have a deep understanding of data processing systems and possess the skills to design and manage them.
Google Professional-Data-Engineer exam is designed to test an individual's knowledge and expertise in the field of data engineering. It is a certification offered by Google that recognizes professionals who have demonstrated their ability to design, build, and maintain data processing systems on the Google Cloud Platform. Professional-Data-Engineer exam covers a wide range of topics, including data ingestion and processing, storage and data analysis, machine learning and data visualization.
NEW QUESTION # 19
Your financial services company is moving to cloud technology and wants to store 50 TB of financial time- series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
Which product should they use to store the data?
- A. Cloud Bigtable
- B. Google Cloud Storage
- C. Google BigQuery
- D. Google Cloud Datastore
Answer: A
Explanation:
https://cloud.google.com/blog/products/databases/getting-started-with-time-series-trend-predictions-using- gcp
NEW QUESTION # 20
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?
- A. Issue a command to restart the database servers.
- B. Reduce the query frequency to once every hour until the database comes back online.
- C. Retry the query with exponential backoff, up to a cap of 15 minutes.
- D. Retry the query every second until it comes back online to minimize staleness of data.
Answer: C
NEW QUESTION # 21
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?
- A. BigQuery
- B. Cloud SQL
- C. Cloud BigTable
- D. Cloud Datastore
Answer: B
NEW QUESTION # 22
You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to BigQuery for analysis. Which job type and transforms should this pipeline use?
- A. Streaming job, PubSubIO, BigQueryIO, side-outputs
- B. Streaming job, PubSubIO, BigQueryIO, side-inputs
- C. Batch job, PubSubIO, side-inputs
- D. Streaming job, PubSubIO, JdbcIO, side-outputs
Answer: C
NEW QUESTION # 23
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?
- A. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
- B. Create a cron schedule in Cloud Dataprep.
- C. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
- D. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
Answer: C
NEW QUESTION # 24
Does Dataflow process batch data pipelines or streaming data pipelines?
- A. None of the above
- B. Only Batch Data Pipelines
- C. Both Batch and Streaming Data Pipelines
- D. Only Streaming Data Pipelines
Answer: C
Explanation:
Dataflow is a unified processing model, and can execute both streaming and batch data pipelines
NEW QUESTION # 25
You are working on a sensitive project involving private user dat
- A. Grant the consultant the Cloud Dataflow Developer role on the project.
- B. Create an anonymized sample of the data for the consultant to work with in a different project.
- C. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?
- D. Grant the consultant the Viewer role on the project.
- E. Create a service account and allow the consultant to log on with it.
Answer: A
NEW QUESTION # 26
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application. What should you do?
- A. Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
- B. Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset
- C. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
- D. Create groups for your users and give those groups access to the dataset
Answer: B
NEW QUESTION # 27
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? (Choose two.)
- A. Publisher throughput quota is too small.
- B. Error handling in the subscriber code is not handling run-time errors properly.
- C. The subscriber code cannot keep up with the messages.
- D. Total outstanding messages exceed the 10-MB maximum.
- E. The subscriber code does not acknowledge the messages that it pulls.
Answer: B,C
NEW QUESTION # 28
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer data. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?
- A. Increase your network bandwidth from your datacenter to GCP.
- B. Increase the size of the Google Persistent Disk on your server.
- C. Increase the CPU size on your server.
- D. Increase your network bandwidth from Compute Engine to Cloud Storage.
Answer: A
NEW QUESTION # 29
Case Study: 2 - MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost. Their management and operations teams are situated all around the globe creating many-to- many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments ?development/test, staging, and production ?
to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community. Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.
Which two actions should you take? (Choose two.)
- A. Adjust the settings for each table to allow a related region-based security group view access.
- B. Adjust the settings for each view to allow a related region-based security group view access.
- C. Ensure each table is included in a dataset for a region.
- D. Adjust the settings for each dataset to allow a related region-based security group view access.
- E. Ensure all the tables are included in global dataset.
Answer: B,C
NEW QUESTION # 30
In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) _____.
- A. VPN connection
- B. SSH tunnel
- C. FTP connection
- D. Special browser
Answer: B
Explanation:
Explanation
To connect to the web interfaces, it is recommended to use an SSH tunnel to create a secure connection to the master node.
Reference:
https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#connecting_to_the_web_interfaces
NEW QUESTION # 31
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?
- A. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
- B. Use the WHERE_PARTITIONTIME pseudo column
- C. Use the TABLE_DATE_RANGE function
- D. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
Answer: C
NEW QUESTION # 32
Case Study: 2 - MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost. Their management and operations teams are situated all around the globe creating many-to- many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments ?development/test, staging, and production ?
to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community. Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
You need to compose visualizations for operations teams with the following requirements:
Which approach meets the requirements?
- A. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
- B. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.
- C. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
- D. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.
Answer: D
NEW QUESTION # 33
Your neural network model is taking days to train. You want to increase the training speed. What can you do?
- A. Subsample your test dataset.
- B. Subsample your training dataset.
- C. Increase the number of layers in your neural network.
- D. Increase the number of input features to your model.
Answer: C
Explanation:
Explanation/Reference: https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-9f5d1c6f407d
NEW QUESTION # 34
You are designing a pipeline that publishes application events to a Pub/Sub topic. Although message ordering is not important, you need to be able to aggregate events across disjoint hourly intervals before loading the results to BigQuery for analysis. What technology should you use to process and load this data to BigQuery while ensuring that it will scale with large volumes of events?
- A. Schedule a Cloud Function to run hourly, pulling all available messages from the Pub/Sub topic and performing the necessary aggregations.
- B. Schedule a batch Dataflow job to run hourly, pulling all available messages from the Pub/Sub topic and performing the necessary aggregations.
- C. Create a Cloud Function to perform the necessary data processing that executes using the Pub/Sub trigger every time a new message is published to the topic.
- D. Create a streaming Dataflow job that reads continually from the Pub/Sub topic and performs aggregations using tumbling windows.
Answer: C
Explanation:
Explanation/Reference:
NEW QUESTION # 35
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings.
Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight dat
- A. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
- B. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
- C. How can you adjust your application design?
- D. Convert the streaming insert code to batch load for individual messages.
- E. Re-write the application to load accumulated data every 2 minutes.
Answer: C
NEW QUESTION # 36
You want to build a managed Hadoop system as your data lake. The data transformation process is composed of a series of Hadoop jobs executed in sequence. To accomplish the design of separating storage from compute, you decided to use the Cloud Storage connector to store all input data, output data, and intermediary dat
a. However, you noticed that one Hadoop job runs very slowly with Cloud Dataproc, when compared with the on-premises bare-metal Hadoop environment (8-core nodes with 100-GB RAM). Analysis shows that this particular Hadoop job is disk I/O intensive. You want to resolve the issue. What should you do?
- A. Allocate sufficient memory to the Hadoop cluster, so that the intermediary data of that particular Hadoop job can be held in memory
- B. Allocate more CPU cores of the virtual machine instances of the Hadoop cluster so that the networking bandwidth for each instance can scale up
- C. Allocate additional network interface card (NIC), and configure link aggregation in the operating system to use the combined throughput when working with Cloud Storage
- D. Allocate sufficient persistent disk space to the Hadoop cluster, and store the intermediate data of that particular Hadoop job on native HDFS
Answer: A
NEW QUESTION # 37
Which of the following are examples of hyperparameters? (Select 2 answers.)
- A. Number of hidden layers
- B. Weights
- C. Number of nodes in each hidden layer
- D. Biases
Answer: A,C
Explanation:
Explanation
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.
Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview
NEW QUESTION # 38
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